ClinicalTrials.gov registration coming within 21 days of first participant being enrolled

Read about our preliminary work on this problem here

...or here (for sedentary behavior).

Physical inactivity is part of a constellation of lifestyle factors – with smoking and diet – that contribute to weight gain in early adulthood. Risk factors that compromise cardiovascular health begin to accumulate during the transition into adulthood. Interventions that prevent decreases in physical activity (PA) during this period can reduce long-term chronic disease risk. Text message interventions have shown a consistent positive effect on PA but efforts to increase those intervention effects via tailoring, targeting or personalizing have not realized their potential. New approaches have emerged for tailoring interventions based on treatment responses or contextual factors (e.g., stepped care, just-in-time adaptive interventions) but they apply a single decision rule uniformly for all participants. Behavior is complex and multiply determined so it is possible that treatment responses are idiosyncratic, necessitating personalized decision rules. Building on interest in precision medicine, we propose a method to develop personalized adaptive messaging interventions using intensive longitudinal data (from wearable sensors and momentary weather indices) and tools from control systems engineering (system identification and robust control synthesis). In preliminary work, we developed a computational model of physical activity responses to individual text messages. The greatest barrier to implementing that approach in interventions is that the computational models required for predictive modeling of PA dynamics have a high degree of uncertainty and are too complex to run efficiently on smartphones and other wearable devices. We propose to solve that problem by (1) developing a dynamical model of physical activity based on historical responses to messages, recent behavior, location-specific weather, and temporal features, and (2) evaluating the acceptability and feasibility of more versus less aggressive adaptation strategies for personalizing an intervention controller. To accomplish these aims, we will recruit young adults to participate in a PA messaging intervention and develop a computational model of responses to different messages under different conditions. A model-based controller will be developed to (a) optimize message timing, frequency, and content selection, and (b) achieve specified behavior change goals under varying conditions. We will then deploy that controller with an independent sample of young adults to determine how more versus less aggressive adaptation strategies over the next six months impact user experience. This study will contribute a model-based intervention controller and an acceptable adaptation strategy to use in a personalized adaptive messaging intervention for increasing PA. If successful, it will increase both PA and user engagement by selecting and timing messages to maximize effects and minimize burden. This approach can be applied to develop personalized interventions for other behaviors relevant for preventing weight gain, preserving cardiovascular health, and reducing chronic disease risk.

Collaborative Research: Data-driven control of switched systems with applications to human behavioral modification

ClinicalTrials.gov registration coming within 21 days of first participant being enrolled

Dramatically increasing health care costs threaten the nation's economy. Over 80% of those costs are due to chronic illnesses which can be prevented or mitigated through lifestyle change. Physical activity is also a key behavioral component of ideal cardiovascular health. This suggests that promoting physical activity through the personalized virtual health advisors can lead to substantial health improvements across a broad spectrum of the population. Motivated by these observations, this proposal seeks to develop a tractable, practical framework for designing personalized behavior monitoring systems, aimed at maintaining optimal levels of physical activity. This is accomplished by embedding the problem into a more general, systems-theoretic one: design of controllers with provable performance for systems characterized by a collection of models where neither the number of models nor their parameters are a priori known and must be obtained from experimental data, collected from multiple sensors with large variations in quality. Education is proactively integrated into this project, starting with STEM summer camps projects for urban middle school students on data driven modeling and continuing at the college level with a multi-disciplinary program that uses personalized medicine to link a full range of distinct subjects ranging from machine learning to systems theory and optimization. At the graduate level, these activities are complemented by recruitment efforts that leverage the resources of Penn State's McNair Scholars Program and Northeastern University's Program in Multicultural Engineering to broaden the participation of underrepresented groups in research.

Motivated by the problem of designing effective behavioral interventions, this proposal seeks to develop a comprehensive, computationally tractable framework for synthesizing data driven control laws for a class of systems described by switched difference inclusions. These models arise in a broad class of domains, ranging from resilient infrastructures to health care, characterized by large amounts of uncertainty and abruptly changing dynamics. The research addresses both the identification and control design problems in a unified framework based on polynomial optimization and its connections to the problem of moments. Contributions to the field of identification include the development of a tractable framework for robust identification of uncertain switched systems that exploits the underlying structure of the problem to substantially reduce the computational complexity and can handle both worst case and risk-adjusted descriptions. Contributions to control include a new framework for chance constrained control of uncertain switched systems that maximizes the probability of achieving a desired final state, while, at the same time, minimizing the probability of entering bad sets. As a proof-of-principle, the resulting framework is applied to the problem of designing smartphone based virtual health advisors capable of providing individualized optimal physical activity strategies.

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